class_docstr: Large-scale distributed sampling face recognition training Module, of course sampling is an option,
  There will be no loss of accuracy in sampling in large-scale identities training tasks, uses only
  8 NVIDIA RTX2080Ti to complete classification tasks with 10 millions of identities, 64 NVIDIA
  RTX2080Ti can complete classification tasks with 100 million of identities.
  
  See the original paper:
  https://arxiv.org/abs/2203.15565
  
  Parameters
  ----------
  symbol: Symbol
      Backbone symbol.
  fc7_model: Object
      Object of margin loss.
  memory_bank: Memory bank Object.
      Object of memory bank, which maintain local class centers and their momentum.
  memory_optimizer: Optimizer object.
      The updater of memory bank, default is sgd optimizer.
  logger:
docstr: Installs and initializes optimizers.
  
  Parameters
  ----------
  optimizer_params : dict
      Default `(('learning_rate', 0.01),)`. The default value is not a dictionary,
      just to avoid pylint warning of dangerous default values.
  force_init : bool
      Default ``False``, indicating whether we should force re-initializing the
      optimizer in the case an optimizer is already installed.
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code: def init_optimizer(self, optimizer_params, force_init=False):
      """
      Installs and initializes optimizers.
  
      Parameters
      ----------
      optimizer_params : dict
          Default `(('learning_rate', 0.01),)`. The default value is not a dictionary,
          just to avoid pylint warning of dangerous default values.
      force_init : bool
          Default ``False``, indicating whether we should force re-initializing the
          optimizer in the case an optimizer is already installed.
      """
      optimizer_backbone = DistributedOptimizer(
          mx.optimizer.SGD(**optimizer_params)
      )
      self.backbone_module.init_optimizer(
          "local", optimizer_backbone, force_init=force_init
      )
  
imports: import logging
  import warnings
  from collections import namedtuple
  import horovod.mxnet
  import mxnet
  import mxnet.ndarray
  import numpy
  from default import config
  from optimizer import DistributedOptimizer
label_desc: Use image node_id 16 for calling the SampleDistributeModule obj's underlying member method code with example usage: obj.init_optimizer(optimizer_params, force_init) without return types